Towards scalable rough set based attribute subset selection for intrusion detection using parallel genetic algorithm in MapReduce

Attribute subset selection based on rough sets is a crucial preprocessing step in data mining and pattern recognition to reduce the modeling complexity. To cope with the new era of big data, new approaches need to be explored to address this problem effectively. In this paper, we review recent work...

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Bibliographic Details
Published in:Simulation modelling practice and theory 2016-05, Vol.64, p.18-29
Main Authors: El-Alfy, El-Sayed M., Alshammari, Mashaan A.
Format: Article
Language:English
Subjects:
Approximation
Attribute subset selection
Big data
Computer information security
Genetic algorithms
Hybrid methods
Intrusion
MapReduce
Minimum reduct
Modelling
Parallel genetic algorithms
Pattern recognition
Preprocessing
Rough sets
Tables (data)
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Summary:Attribute subset selection based on rough sets is a crucial preprocessing step in data mining and pattern recognition to reduce the modeling complexity. To cope with the new era of big data, new approaches need to be explored to address this problem effectively. In this paper, we review recent work related to attribute subset selection in decision-theoretic rough set models. We also introduce a scalable implementation of a parallel genetic algorithm in Hadoop MapReduce to approximate the minimum reduct which has the same discernibility power as the original attribute set in the decision table. Then, we focus on intrusion detection in computer networks and apply the proposed approach on four datasets with varying characteristics. The results show that the proposed model can be a powerful tool to boost the performance of identifying attributes in the minimum reduct in large-scale decision systems.
ISSN:1569-190X
1878-1462
DOI:10.1016/j.simpat.2016.01.010